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Found 14 papers, 3 papers with code
Adaptive Block Sparse Regularization under Arbitrary Linear Transform
We propose a convex and fast signal reconstruction method for block sparsity under arbitrary linear transform with unknown block structure.
ADMM-MM Algorithm for General Tensor Decomposition
In this paper, we propose a new unified optimization algorithm for general tensor decomposition which is formulated as an inverse problem for low-rank tensors in the general linear observation models.
Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records
In medical image diagnosis, identifying the attention region, i. e., the region of interest for which the diagnosis is made, is an important task.
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches
This study proposes a framework for manifold learning of image patches using the concept of equivalence classes: manifold modeling in quotient space (MMQS).
Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space
Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling.
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning
To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed.
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"
The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.
Computing Valid p-values for Image Segmentation by Selective Inference
To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework to compute valid p-values in which the segmentation bias is properly accounted for.
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space
The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization.
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization
In the sense of trade-off tuning, the noisy tensor completion problem with the `noise inequality constraint' is better choice than the `regularization' because the good noise threshold can be easily bounded with noise standard deviation.
Accurate and Robust Registration of Nonrigid Surface Using Hierarchical Statistical Shape Model
In this paper, we propose a new non-rigid robust registration method that registers a point distribution model (PDM) of a surface to given 3D images.
